Company
AI & Data Engineering : Data Science
SeniorDataScientist
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Data Scientist. Skills: AI Engineering, MLOps, LLM integrations, Agentic AI workflows. Build enterprise AI solutions. Integrate LLMs”
What You'll Achieve.
Deliver internal enterprise AI solutions; Meet scale requirements; Meet security requirements; Meet reliability requirements; Meet engineering standards; Meet compliance standards; Meet responsible AI standards; Support production readiness; Support production stability; Identify internal productivity opportunities; Identify internal cost-saving opportunities
Industry & Context.
Technical debt remediation; System observability; Scalability improvements; Feasibility assessments; Build complexity assessments; Integration requirements assessments
What They're Looking For.
Must Have
5+ years software engineering experience, 5+ years AI/ML engineering experience, Hands-on AI/ML stack experience, Experience developing AI applications in cloud environments, Proficiency in Python, Familiarity with enterprise cloud AI platforms, Familiarity with AI orchestration tooling, Ability to work within defined architectural standards
Nice to Have
LLMs experience preferred, Retrieval-augmented generation experience preferred, Agent frameworks experience preferred, MLOps pipelines experience preferred, Exposure to enterprise AI platforms, Exposure to Google Workspace AI, Exposure to Gemini Enterprise
What You'll Do.
Build enterprise AI solutions
Develop agentic AI workflows
Support Gemini Enterprise features
Support data pipelines
Develop code in Python
Follow AI engineering standards
Participate in technical design reviews
Contribute implementation input
Prototype new AI frameworks
Prototype new AI platforms
Provide input on findings
Support technical recommendations
Contribute to LLM abstraction layer
Contribute to multi-model integrations
Support technical debt remediation
Support system observability
Support scalability improvements
Support AI intake process
Contribute technical assessments
Ensure AI initiatives meet standards
Develop AI solutions against specifications
Build integrations across enterprise systems
Implement human-in-the-loop features
Implement explainability features
Implement auditability features
Support AI agent lifecycle management
Manage AI agent feedback loops
Improve AI agents continuously
Contribute to technical deployment
Operationalize AI agents
Operationalize AI solutions
Support production readiness
Support production stability
Support Gemini Enterprise adoption
Participate in AI Friday sessions
Build engineering dashboards
Maintain engineering dashboards
Track internal AI system performance
Communicate engineering decisions
Communicate solution outcomes
Research emerging AI techniques
Research emerging ML techniques
Research large language models
Research agent frameworks
Research enterprise tooling
Identify productivity opportunities
Identify cost-saving opportunities
Contribute to proof-of-concept pilots
Validate internal use cases
Maintain AI innovation pipeline
Contribute to AI innovation pipeline
Transformation & Financial Flexibility team
Support identification of cost-saving opportunities
Support technical validation of cost-saving opportunities
Act as technical contributor
Act as AI subject matter resource
Build collaborative relationships
How You'll Work.
Team & Collaboration
Cross-functional partnership; Technical peers; Team leads; Transformation & Financial Flexibility team; Business units; Engineering; Security; Compliance; Legal; HR
Communication Scope
Communicate decisions; Communicate outcomes; Knowledge sharing
Full Job Description
## Description Data Scientist ## Primary Skills Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio ## Specialization Data Science Advanced: Data Specialist ## Job requirements AI Developer Anaplan Key Responsibilities AI Engineering & Development • Build and deliver internal enterprise AI solutions — including LLM integrations, agentic AI workflows, Gemini Enterprise features, and supporting data pipelines — designed for the scale, security, and reliability requirements of a global workforce. • Develop code in Python following the AI engineering standards, coding practices, and technical guardrails. • Participate in technical design reviews and contribute implementation input during intake, design, and production deployment phases. • Evaluate and prototype new AI tools, frameworks, and platforms, providing input on findings to support technical recommendations. • Contribute to the development and maintenance of the LLM abstraction layer and multi-model integrations. • Support technical debt remediation, system observability, and scalability improvements for Anaplan’s internal AI systems. AI Platform Governance & Enablement • Support the AI intake process by contributing technical assessments of feasibility, build complexity, and integration requirements for employee-facing tools. • Ensure all assigned internal AI initiatives meet engineering, security, compliance, and responsible AI standards before and during development. • Develop AI solutions against clear technical specifications • Build and maintain integrations across core enterprise systems (Salesforce, Servi
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